Optimal design of SSSC and crowbar parameters for performance enhancement of Egyptian Zafrana wind farm

At the beginning of this century, Egypt intended to the expansion of the establishment of industrial cities. This has led to an increase in the demand of electrical energy needed for these industrial cities. The main challenge which faces the Egyptian government is how to increase the electric energy production without increase the fuel cost. The expanding in generating electric energy from renewable energy is the best chose to face this challenge. The geographical location of Egypt has granted Egypt advantages such as solar radiation reaches 24 MJ/m² [1, 2], and wind speed reaches 15 m/s at coastal areas [3]. This engorges Egyptian government to develop plans for the establishment of renewable energy plants. It is known that the generating electricity from wind energy is lower cost than the generating electricity from solar energy. Hence, Egyptian government starts their steps by installing wind farms, especially in Red Sea coast. Hence, this paper focuses on wind farms which have been installed and interconnected with the Egypt national electric network (ENEN).

There are many articles that studied the issue of enhancing the relation between wind farms and their interconnected grids. These efforts revolve about using flexible AC transmission system (FACTS) to improve the impact of wind farms interconnected grid or using artificial intelligence methods to improve the performance of FACTS integrated with wind farms. In this paper, literature review will focus on the efforts which have been achieved to improve the performance of wind farms based on SCIG (SCIG-WF). A proposed control method based on hierarchal PI-fuzzy-PI controller was discussed in [4] to improve the frequency control of SCIG-WF during an unbalance between load demand and power generated. In Ref. [5], Static Synchronous Compensator (STATCOM) was used to regulate the voltage of SCIG-WF during fault condition. Enhancing the transient stability of SCIG-WF by improving the control system of STATCOM was introduced in [6]. The artificial neural network (ANN) was proposed to find the optimal value of STATCOM controller in [7]. The optimal place of integrated unified power quality conditioner (i-UPQC) was investigated in [8] to improve the power quality of distribution system interconnected to SCIG-WF. Static Synchronous Series Compensator (SSSC) based on ANN was used to improve different type of wind turbines including SCIG wind farms in [9]. Low Voltage Ride Through (LVRT) capability of SCIG-WF was improved using unified inter-phase power controller (UIPC) in [10]. In Ref. [11], Center-node Unified Power Flow Controller was used to enhance different types of wind farms even SCIG-WF. The crowbar was used to improve the relationship between wind farms and interconnected grid. Most of these articles were based on using crowbar as inner protection to stator of doubly fed induction generators (DFIG) such as in [12‐14]. In Ref. [15], crower is used as outer protection to improve the impact of wind farm base on SCIG and DFIG. The performance and load flow study of Zafarana wind farm was presented in [16]. The information and communication technology (ICT)-based ANN was investigated in [17] to improve the control system of Zafarana wind farm. Improving the performance of Zafarana wind farm using STATCOM was investigated in [18]. The impact of Harmonic resonance due to overvoltage on the transformers connected the Zafarana wind farm to ENEN was studied in [19].

LAPO algorithm is an efficient physical-based technique which conceptualizes from behavior of Lightening in natural [20, 21]. LAPO algorithm was successfully implemented for solving many optimization problems where in [22] LAPO has been used for Optimal Power Flow problem (OPF) solution with STATCOM. The authors in [22] implemented the LAPO algorithm for the wrapper feature selection optimizations. In [22], LAPO algorithm was utilized to determine the most fitted parameters of the UPFC device. In [23], the OPF problem has been solved by the LAPO.

Sine cosine algorithm (SCA) technique is a new population-based optimization technique proposed by Mirjalili [24]. The SCA is also applied for solving many optimization problems where the authors In [25], solved optimization the bend photonics crystal. Attia et al. [26] implemented the SCA for OPF solution. The authors in [27] solved the optimal reactive power dispatch problem with inclusion of series and shunt controller using the SCA.

SMC features with its ability to control the performance of systems without concerning the effect of uncertainty factors. The effect of the phenomena of chattering represents the major disadvantage of this type of control. There are some articles which deal with this issue to overcome the effect of these phenomena such as [28]. In [29], SMC with adjusting output feedback was used to control the power and speed of wind turbines. A hybrid of fuzzy logic control and second-order SMC was applied to unified power flow controller in [30]. In [31], a developed method of SMC was proposed to reduce the phenomena of chattering and investigate the optimal power extracted from wind.

In this paper, the unsatisfactory performance of Zafarana wind farm especially disconnection from ENEN during fault condition is handled using proposed Cro-SSSC. The Cro-SSSC is composed of a combination of crowbar and SSSC. The crowbar is combined with SSSC to protect the SSSC from being damaged due to high current during fault where SSSC is connected in series with the transmission line. In this paper, LAPO and SCA algorithms are used to determine the appropriate values of the crowbar resistance and control parameters of SSSC. These values of the crowbar resistance and control parameters of SSSC are determined to fit with studied system of Zafarana1 wind farm. Also, SMC is proposed to control the SSSC based on the values of the control parameters of SSSC obtained by optimization. The performance of Zafarana1 wind farm with Cro-SSSC when it is tuned by LAPO, SCA and SMC is compared with Zafarana1 wind farm Z1 without Cro-SSSC during three phase and single line to ground faults.

Figure 5 shows the SLD of SSSC control system. As shown in Fig. 5, the phase angle is generated by the phase-locked loop (PLL). The θ is used to generate the quadratic component Vqm and Vqn of Vm and Vn.

The actual quadratic component of voltage of m and n Vqm and Vqn buses are computed to produce actual quadratic component (Vq_meg). This actual quadratic component is compared with the quadratic component of the reference voltage (Vq-ref). The result of this comparison is the signal which is injected to the AC voltage regulator. The AC voltage regulator generates the quadratic component of the SSSC’s voltage (Vqsssc). The direct component of the SSSC’s voltage (Vdsssc) is generated by comparing the voltage of DC bus with the reference voltage (Vdc-ref) and injecting the output DC voltage regulator. Figure 6 shows the gains of AC and DC voltage regulators (Kpvac and Kivac) and (Kpvdc and Kivdc) of SSSC.

From Fig. 6 the injected voltage of SSSC (\(V_{sssc}\)) can be concluded as follows:

SMC) is a type of modern nonlinear control. The importance of (SMC) is its ability to ensure that the control system works efficiently without being affected by uncertain factors [44]. In [45], SMC to control the grid and rotor side converters of DFIG based on fuzzy control to tune PI controller. In this paper, SMC is proposed to control the AC and DC voltage regulators of SSSC based on LAPO to tune PI controller. As shown in Fig. 9, the proposed control signals (u_{smc_vac} and u_{smc_vdc}) are add to AC voltage and DC voltage regulators of SSSC, respectively.

To investigate the SMC of AC and DC voltage regulator, the following steps will be followed. The dynamic equation of V_qsssc is defined as follows:where F is the disturbance occurred during operation condition. The error signal is given by

By adding the additional control signal of SMC (u_{smc_vac}) and substituting Eqs. (29) into (10), Eq. (10) can be written as follows:

The switching equation can be defined as follows [45]:

The state space of Eq. (31) and derivative of S can be defined as follows:

In order to ensure that the system will follow the sliding surface, u_smc has two components u_smc1 and u_smc2; where u_{smc_vac} = u_{smc_ac1} + u_{smc_ac2}. u_{smc_ac1} represents the ideal condition of the sliding mode, and u_{smc_ac2} is used to reduce the effects of chattering. The ideal condition of the sliding mode will occur when S = 0. u_{smc_ac1} will be equal to the next equation.

In order to reduce the effects of chattering, u_smc2 will be defined as follows [28]:

By substituting Eqs. (35) and (36) into (30), the V_qsssc can be written as follows:

By the same way, the SMC of DC voltage regulator can be investigated and Eq. (9) can be written as follows:

In order to obtain optimal values of Cro-SSSC using LAPO and SCA, the properties and features of MATLAB program are used to link M-file scripts of LAPO and SCA with proposed MATLAB–Simulink model of Zafarana Z1 wind farm integrated with Cro-SSSC. Table 4 shows the optimal values of proposed Cro-SSSC. Also, the optimal values of proposed Cro-SSSC when SMC is used as an example of nun-liner control method.

Figure 10 shows the active power of Zafarana Z1 wind farm during three-phase fault when it connected with and without Cro-SSSC. As shown in Fig. 10 when the Zafarana Z1 wind farm is integrated with Cro-SSSC, the active power generated by is 21 MW during the fault period.

This means that it has not separated from the ENEN due to the performance of Cro-SSSC. Unlike the non-use of Cro-SSSC, the active power of the Zafarana Z1 drops to zero MW. This means, the Zafarana Z1 suffers from complete separation from the ENEN. Also, Fig. 10 shows that the Cro-SSSC has almost the same performance when it is tuned by LAPO and SCA. Also, Fig. 10 shows when SMC is used as nonlinear control method to control Cro-SSSC has improved the active power produced by Zafarana Z1 and injects more power to ENEN during the fault. There must be a reason for the dropping in active power of Zafarana Z1 wind farm when there is no connection to Cro-SSSC. To discover this reason, the behaviors of voltage and current have to be observed and studied. Figures 11 and 12 show the voltage and current of Zafarana Z1 wind farm during fault condition.

As shown in Fig. 11, when the Cro-SSSC has not been used, the voltage of Zafarana Z1 wind farm is dropped to zero. On the other hand, when of Zafarana Z1 wind farm is integrated with the Cro-SSSC, the voltage is only dropped to 0.8 pu and 0.98 pu when SMC is used. Also, the performance of Cro-SSSC when it is tuned by LAPO or SCA is almost the same. As shown in Figs. 10 and 11, both of active power and voltage were dropped to zero from the first moment of fault occurrence. Hence, the time of disconnection of Zafarana Z1 wind farm from ENEN can be observed. The time of disconnection of Zafarana Z1 wind farm from ENEN can be observed by studying the behavior of current as shown in Fig. 12.

Figure 12 shows that when Zafarana Z1 wind farm is integrated with Cro-SSSC, the lowest value of current is 0.8 pu for both LAPO and SCA. When SMC is used, the lowest value of current is 1.09 pu which sets in the allowable limits of protection system. On other hand, when the Cro-SSSC has not been used the current of Zafarana Z1 wind farm is increased to 2.5 pu at the begging of fault then decreased to 1.5 pu. After the occurrence of fault by 0.1 s (at time equal to 25.105 s) the current is dropped to zero. This means that the protection system has disconnected the Zafarana Z1 wind farm from after the delay time of the fastest protection. According to Table 3 the fast protection is the under voltage protection which will disconnect the Zafarana Z1 wind after 0.1 s, this means that the disconnection is due to the under voltage. The behavior of current at the connection bus will affect the reactive power absorbed by the Zafarana Z1 wind farm ENEN. This is shown in Fig. 13 which shows the reactive power of the Zafarana Z1 wind farm at the connection bus.

When the Zafarana Z1 wind farm is integrated with the Cro-SSSC which is tuned by LAPO, SCA and SMC, the absorbed reactive power of the Zafarana Z1 wind farm is decreased during fault. The reactive power of the Zafarana Z1 has returned to steady state value much faster when Cro-SSSC is controlled by SMC. This decreasing in the absorbed reactive power is consistent with the decreasing in current in case of Zafarana Z1 wind farm is integrated with the Cro-SSSC. Consequently, the increase in current of Zafarana Z1 wind farm without suing Cro-SSSC will cause increase in the absorbed reactive power by Zafarana Z1 wind farm during fault. This is cleared in Figs. 12 and 13. The performance of current and reactive power of Zafarana Z1 wind farm during fault will reflect on the rotor speed of SCIG wind turbines which are used in Zafarana Z1 wind farm. Figure 14 shows the rotor speed of SCIG wind turbines. As it is cleared from Fig. 14 when the Cor-SSSC has not been used, the rotor speed is increased as the absorbed reactive power by Zafarana Z1 wind farm increases. In an inverse way, when the Cro-SSSC is used, it can be said that the change in rotor speed is not noticeable, especially when Cro-SSSC is controlled by SMC.

The impact of Cro-SSSC on parameters of Zafarana Z1 wind farm is almost the same in both case of tuning (LAPO and SCA). One the other hand, when Cro-SSSC is controlled using SMC based on optimization LAPO, this impact is almost non-existent. This means the change in the voltage of DC bus of SSSC will be almost the same in both case of tuning (LAPO and SCA). Figure 15 shows the voltage of DC bus of Cro-SSSC will be almost the same in both case of tuning (LAPO and SCA). As shown in Fig. 15, the impact of using LAPO to tune Cro-SSSC on voltage of DC bus of Cro-SSSC is very close to the impact of using SCA to tune Cro-SSSC.

Also, Fig. 15 shows the ability of the SMC to make the Cro-SSSC follows the path of the sliding surface.

Figures 16 and 17 show the active power and current of Zafarana Z1 wind farm at the connection bus. As it can be observed, in all cases the Zafarana Z1 wind farm is still connected to the grid. The worst case happened in the absence of Cro-SSSC, while the best performance is for Cro-SSSC controlled by SMC based on LAPO.

When LAPO and SCA are used to tune Cro-SSSC, the performance of Zafarana Z1 is almost identical and better than the case of absence of Cro-SSSC. Also, Figs. 16 and 17 show that in the case of Cro-SSSC controlled by SMC based on LAPO, the change in active power and current due to single line fault is very small. According to [46], the absence of one phase will increase the voltage in the other two phase, and hence, the root mean square (rms) value of voltage will be higher than the nominal value; this will happen if the reactive power is increased. This is shown in Fig. 18 which shows voltage of Zafarana Z1 wind farm and Fig. 19 which shows the reactive power of Zafarana Z1 wind farm (single line to ground fault). In the absence of single phase and increasing in absorbed reactive power the rms of voltage will increase in the case of LAPO, SCA and SMC due to the presence of Cro-SSSC. On other hand, when Cro-SSSC is not used, the voltage is decreased and the reactive power is injected from the capacitor bank connected to SCIG wind turbines of Zafarana Z1 wind farm. From Fig. 20, it can be observed that change in DC voltage of Cro-SSSC is from 0.995 to 0.005 pu. Also, the change in case of SMC is lower the LAPO and SCA. This is due to the impact of SMC which drives the system to around sliding surface. Hence, the change in rotor speed of SCIG wind turbines of Zafarana Z1 wind farm is very small as shown in Fig. 21.